Multitask Imitation Learning Algorithm Based on Composite Covariance Function

To acquire the multitask robot imitation learning control strategy,a Gauss process regression( GPR) model was established to express the control strategy,a composite covariance function was constructed,and the sample points of the teaching behavior was used to optimized the hyperparameters in the GPR model. The control strategy was applied by the imitation robot to accomplish the imitation task.The Braitenberg vehicles were used as simulation object to research multitask( phototaxis and obstacle avoidance tasks) imitation learning. Simulation results indicate that compared with the imitation learning algorithm based on the single covariance function,the imitation learning algorithm based on the composite covariance function can not only realize single task imitation learning,but also realize multitask imitation learning,and the precision is higher. The simulation results in various task environments indicate that the method is adaptive.